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-rw-r--r--training/lbfgs_test.cc7
-rw-r--r--training/mpi_flex_optimize.cc145
-rw-r--r--training/optimize.cc3
3 files changed, 100 insertions, 55 deletions
diff --git a/training/lbfgs_test.cc b/training/lbfgs_test.cc
index fc21e98d..c94682e9 100644
--- a/training/lbfgs_test.cc
+++ b/training/lbfgs_test.cc
@@ -28,11 +28,14 @@ double TestOptimizer() {
g[2] = 2 * x[2] + 6;
obj = 4 * x[0]*x[0] + x[0] * x[1] + x[1]*x[1] + x[2]*x[2] + 6 * x[2] + 5;
opt.run(x, obj, g);
-
+ if (!opt.requests_f_and_g()) {
+ if (converged(x,g)) break;
+ opt.run(x, obj, g);
+ }
cerr << x[0] << " " << x[1] << " " << x[2] << endl;
cerr << " obj=" << obj << "\td/dx1=" << g[0] << " d/dx2=" << g[1] << " d/dx3=" << g[2] << endl;
cerr << opt << endl;
- } while (!converged(x, g));
+ } while (true);
return obj;
}
diff --git a/training/mpi_flex_optimize.cc b/training/mpi_flex_optimize.cc
index 87c5f331..00746532 100644
--- a/training/mpi_flex_optimize.cc
+++ b/training/mpi_flex_optimize.cc
@@ -39,15 +39,12 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("weights,w",po::value<string>(),"Initial feature weights")
("training_data,d",po::value<string>(),"Training data")
("minibatch_size_per_proc,s", po::value<unsigned>()->default_value(6), "Number of training instances evaluated per processor in each minibatch")
- ("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (options: lbfgs, sgd, rprop)")
- ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch (1 = standard SGD)")
+ ("minibatch_iterations,i", po::value<unsigned>()->default_value(10), "Number of optimization iterations per minibatch")
("iterations,I", po::value<unsigned>()->default_value(50), "Number of passes through the training data before termination")
+ ("regularization_strength,C", po::value<double>()->default_value(0.2), "Regularization strength")
+ ("time_series_strength,T", po::value<double>()->default_value(0.0), "Time series regularization strength")
("random_seed,S", po::value<uint32_t>(), "Random seed (if not specified, /dev/random will be used)")
- ("lbfgs_memory_buffers,M", po::value<unsigned>()->default_value(10), "Number of memory buffers for LBFGS history")
- ("eta_0,e", po::value<double>()->default_value(0.1), "Initial learning rate for SGD")
- ("L1,1","Use L1 regularization")
- ("L2,2","Use L2 regularization")
- ("regularization_strength,C", po::value<double>()->default_value(1.0), "Regularization strength (C)");
+ ("lbfgs_memory_buffers,M", po::value<unsigned>()->default_value(10), "Number of memory buffers for LBFGS history");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
@@ -64,7 +61,7 @@ bool InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::notify(*conf);
if (conf->count("help") || !conf->count("training_data") || !conf->count("cdec_config")) {
- cerr << "General-purpose minibatch online optimizer (MPI support "
+ cerr << "LBFGS minibatch online optimizer (MPI support "
#if HAVE_MPI
<< "enabled"
#else
@@ -166,6 +163,38 @@ void AddGrad(const SparseVector<prob_t> x, double s, SparseVector<double>* acc)
acc->add_value(it->first, it->second.as_float() * s);
}
+double PNorm(const vector<double>& v, const double p) {
+ double acc = 0;
+ for (int i = 0; i < v.size(); ++i)
+ acc += pow(v[i], p);
+ return pow(acc, 1.0 / p);
+}
+
+void VV(ostream&os, const vector<double>& v) {
+ for (int i = 1; i < v.size(); ++i)
+ if (v[i]) os << FD::Convert(i) << "=" << v[i] << " ";
+}
+
+double ApplyRegularizationTerms(const double C,
+ const double T,
+ const vector<double>& weights,
+ const vector<double>& prev_weights,
+ vector<double>* g) {
+ assert(weights.size() == g->size());
+ double reg = 0;
+ for (size_t i = 0; i < weights.size(); ++i) {
+ const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
+ const double& w_i = weights[i];
+ double& g_i = (*g)[i];
+ reg += C * w_i * w_i;
+ g_i += 2 * C * w_i;
+
+ reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
+ g_i += 2 * T * (w_i - prev_w_i);
+ }
+ return reg;
+}
+
int main(int argc, char** argv) {
#ifdef HAVE_MPI
mpi::environment env(argc, argv);
@@ -176,7 +205,7 @@ int main(int argc, char** argv) {
const int size = 1;
const int rank = 0;
#endif
- if (size > 1) SetSilent(true); // turn off verbose decoder output
+ if (size > 0) SetSilent(true); // turn off verbose decoder output
register_feature_functions();
MT19937* rng = NULL;
@@ -186,56 +215,60 @@ int main(int argc, char** argv) {
boost::shared_ptr<BatchOptimizer> o;
const unsigned lbfgs_memory_buffers = conf["lbfgs_memory_buffers"].as<unsigned>();
-
- istringstream ins;
- ReadConfig(conf["cdec_config"].as<string>(), &ins);
- Decoder decoder(&ins);
-
- // load initial weights
- vector<weight_t> init_weights;
- if (conf.count("weights"))
- Weights::InitFromFile(conf["weights"].as<string>(), &init_weights);
+ const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
+ const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();
+ const double regularization_strength = conf["regularization_strength"].as<double>();
+ const double time_series_strength = conf["time_series_strength"].as<double>();
+ const bool use_time_series_reg = time_series_strength > 0.0;
+ const unsigned max_iteration = conf["iterations"].as<unsigned>();
vector<string> corpus;
vector<int> ids;
ReadTrainingCorpus(conf["training_data"].as<string>(), rank, size, &corpus, &ids);
assert(corpus.size() > 0);
- const unsigned size_per_proc = conf["minibatch_size_per_proc"].as<unsigned>();
if (size_per_proc > corpus.size()) {
- cerr << "Minibatch size must be smaller than corpus size!\n";
+ cerr << "Minibatch size (per processor) must be smaller or equal to the local corpus size!\n";
return 1;
}
- size_t total_corpus_size = 0;
-#ifdef HAVE_MPI
- reduce(world, corpus.size(), total_corpus_size, std::plus<size_t>(), 0);
-#else
- total_corpus_size = corpus.size();
-#endif
+ // initialize decoder (loads hash functions if necessary)
+ istringstream ins;
+ ReadConfig(conf["cdec_config"].as<string>(), &ins);
+ Decoder decoder(&ins);
+
+ // load initial weights
+ vector<weight_t> prev_weights;
+ if (conf.count("weights"))
+ Weights::InitFromFile(conf["weights"].as<string>(), &prev_weights);
if (conf.count("random_seed"))
rng = new MT19937(conf["random_seed"].as<uint32_t>());
else
rng = new MT19937;
- const unsigned minibatch_iterations = conf["minibatch_iterations"].as<unsigned>();
+ size_t total_corpus_size = 0;
+#ifdef HAVE_MPI
+ reduce(world, corpus.size(), total_corpus_size, std::plus<size_t>(), 0);
+#else
+ total_corpus_size = corpus.size();
+#endif
- if (rank == 0) {
+ if (rank == 0)
cerr << "Total corpus size: " << total_corpus_size << endl;
- const unsigned batch_size = size_per_proc * size;
- }
- SparseVector<double> x;
- Weights::InitSparseVector(init_weights, &x);
CopyHGsObserver observer;
int write_weights_every_ith = 100; // TODO configure
int titer = -1;
- vector<weight_t>& lambdas = decoder.CurrentWeightVector();
- lambdas.swap(init_weights);
- init_weights.clear();
+ vector<weight_t>& cur_weights = decoder.CurrentWeightVector();
+ if (use_time_series_reg) {
+ cur_weights = prev_weights;
+ } else {
+ cur_weights.swap(prev_weights);
+ prev_weights.clear();
+ }
int iter = -1;
bool converged = false;
@@ -243,26 +276,20 @@ int main(int argc, char** argv) {
#ifdef HAVE_MPI
mpi::timer timer;
#endif
- x.init_vector(&lambdas);
++iter; ++titer;
-#if 0
if (rank == 0) {
converged = (iter == max_iteration);
- Weights::SanityCheck(lambdas);
- Weights::ShowLargestFeatures(lambdas);
string fname = "weights.cur.gz";
if (iter % write_weights_every_ith == 0) {
- ostringstream o; o << "weights.epoch_" << (ai+1) << '.' << iter << ".gz";
+ ostringstream o; o << "weights.epoch_" << iter << ".gz";
fname = o.str();
}
- if (converged && ((ai+1)==agenda.size())) { fname = "weights.final.gz"; }
+ if (converged) { fname = "weights.final.gz"; }
ostringstream vv;
- vv << "total iter=" << titer << " (of current config iter=" << iter << ") minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << x.size() << '/' << FD::NumFeats() << " passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size())) << " eta=" << lr->eta(titer);
+ vv << "total iter=" << titer << " (of current config iter=" << iter << ") minibatch=" << size_per_proc << " sentences/proc x " << size << " procs. num_feats=" << FD::NumFeats() << " passes_thru_data=" << (titer * size_per_proc / static_cast<double>(corpus.size()));
const string svv = vv.str();
- cerr << svv << endl;
- Weights::WriteToFile(fname, lambdas, true, &svv);
+ Weights::WriteToFile(fname, cur_weights, true, &svv);
}
-#endif
vector<Hypergraph> hgs(size_per_proc);
vector<Hypergraph> gold_hgs(size_per_proc);
@@ -287,8 +314,8 @@ int main(int argc, char** argv) {
Hypergraph& hg_gold = gold_hgs[i];
if (hg.edges_.size() < 2) continue;
- hg.Reweight(lambdas);
- hg_gold.Reweight(lambdas);
+ hg.Reweight(cur_weights);
+ hg_gold.Reweight(cur_weights);
SparseVector<prob_t> model_exp, gold_exp;
const prob_t z = InsideOutside<prob_t,
EdgeProb,
@@ -324,23 +351,37 @@ int main(int argc, char** argv) {
#endif
local_grad.clear();
if (rank == 0) {
- g /= (size_per_proc * size);
+ // g /= (size_per_proc * size);
if (!o)
o.reset(new LBFGSOptimizer(FD::NumFeats(), lbfgs_memory_buffers));
vector<double> gg(FD::NumFeats());
- if (gg.size() != lambdas.size()) { lambdas.resize(gg.size()); }
+ if (gg.size() != cur_weights.size()) { cur_weights.resize(gg.size()); }
for (SparseVector<double>::const_iterator it = g.begin(); it != g.end(); ++it)
if (it->first) { gg[it->first] = it->second; }
- cerr << "OBJ: " << obj << endl;
- o->Optimize(obj, gg, &lambdas);
+ g.clear();
+ double r = ApplyRegularizationTerms(regularization_strength,
+ time_series_strength * (iter == 0 ? 0.0 : 1.0),
+ cur_weights,
+ prev_weights,
+ &gg);
+ obj += r;
+ if (mi == 0 || mi == (minibatch_iterations - 1)) {
+ if (!mi) cerr << iter << ' '; else cerr << ' ';
+ cerr << "OBJ=" << obj << " (REG=" << r << ")" << " |g|=" << PNorm(gg, 2) << " |w|=" << PNorm(cur_weights, 2);
+ if (mi > 0) cerr << endl << flush; else cerr << ' ';
+ } else { cerr << '.' << flush; }
+ // cerr << "w = "; VV(cerr, cur_weights); cerr << endl;
+ // cerr << "g = "; VV(cerr, gg); cerr << endl;
+ o->Optimize(obj, gg, &cur_weights);
}
#ifdef HAVE_MPI
- broadcast(world, x, 0);
+ // broadcast(world, x, 0);
broadcast(world, converged, 0);
world.barrier();
if (rank == 0) { cerr << " ELAPSED TIME THIS ITERATION=" << timer.elapsed() << endl; }
#endif
}
+ prev_weights = cur_weights;
}
return 0;
}
diff --git a/training/optimize.cc b/training/optimize.cc
index f0740d5c..41ac90d8 100644
--- a/training/optimize.cc
+++ b/training/optimize.cc
@@ -96,6 +96,7 @@ void LBFGSOptimizer::OptimizeImpl(const double& obj,
const vector<double>& g,
vector<double>* x) {
opt_.run(&(*x)[0], obj, &g[0]);
- cerr << opt_ << endl;
+ if (!opt_.requests_f_and_g()) opt_.run(&(*x)[0], obj, &g[0]);
+ // cerr << opt_ << endl;
}
generated by cgit v1.2.3 (git 2.46.0) at 2025-10-31 11:01:03 +0000